Much effort has been spent over the last decade in producing so called "Machine Vision" systems for use in robotics, automated inspection, assembly and numerous other fields. Because of the large amount of data involved in an image (typically ¼ MByte) and the complexity of many algorithms used, the processing times required have been far in excess of real time on a VAX-class serial processor. We review a number of image understanding algorithms that compute a globally defined "state", and show that they may be computed using simple local operations that are suited to parallel implementation. In recent years, many massively parallel machines have been designed to apply local operations rapidly across an image. We review several vision machines. We develop an algebraic analysis of the performance of a vision machine and show that, contrary to the commonly-held belief, the time taken to relay images between serial streams can exceed by far the time spent processing. We proceed to investigate the roles that a variety of pipelining techniques might play. We then present three pipelined designs for vision, one of which has been built. This is a parallel pipelined bit slice convolution processor, capable of operating at video rates. This design is examined in detail, and its performance analysed in relation to the theoretical framework of the preceeding chapters. The construction and debugging of the device, which is now operational in its hardware is detailed.